\input{format.tex}

%--------------------------------------------------------------------
\begin{abstract}
When building distributed systems that spread over a wide geographical area, network latency is an important factor to choose locations. An optimal location should maintain a small enough latency both to other datacenters and to the client area it intends to serve. To meet this need, we develop a method to evaluate network latency between two arbitrary geographic locations and apply it to a large area to get accurate relationship between network latency and distance in different areas. Some previous works place very loosely general constraints on the relationship between network latency and geographical distance (e.g. a constant based on the speed of light). To the best of our knowledge, we are the first to quantify this information in a region basis. We also provide a browser-based map that allowing users to query network latencies between two arbitrary geographical locations.  


non-intrusive measurement

Based on direct online measurement

Wide-spread

Can be repeated quickly to reflect changes in Internet
\end{abstract}
%--------------------------------------------------------------------
\section{Introduction}
In many cases we want to know the network latency between two arbitrary points. Example: distributed system location selection, cost optimization, network emulator.

a internet latency map providing latency between two arbitrary geographic locations. 

Motivation: provide a quantative result to design of large-scale distributed systems. Basic idea of internet weather

Introduction to RIPE Atlas

Advantage:  accuracy, large-scale coverage, non-Intrusive measurement, fast. As internet is evolving everyday, refreshed result should be able to be retrived in a short time interval.   

Basic idea: with international network provided by RIPE Atlas, we draw a traceroute map that theoretically near both end of the location to be measured. Network latency

Comparing to previous methods like \cite{gummadi_2002}, with the support of RIPE Atlas network, our method covers more IP nodes, and thus provide a higher accuracy. 

Instead of using ping to evaluate the network latency, we make use traceroute with paris support. As is mentioned in \cite{pelsser_2013}, traceroute results provide better stability and accuracy than ping. Also, with a single traceroute we can retrieve latency data between different nodes on a path, which enable us to collect more IPs.

\section{What people already did?}

\cite{fan_2010} talks about selecting IP addresses targets for Internet topology test. They choose at least one IP address per \\24 prefix. 

\cite{gummadi_2002} use recursive DNS query to accurately evaluate latency between two DNS server, and further evaluate latencies between two Internet hosts.

\cite{calder_2013} look for all Google servers by sending DNS queries from clients belong to different IPv4 prefixs. They use MaxMind free geolocation data and airport code in domain name to loosely locate the server. Then use latencies from different clients to further locate the geolocation. 

\cite{pelsser_2013} report that ping is not suitable for measurement because it is unable to deal with load-balance networks. Instead, paris-traceroute should be used.

\cite{padmanabhan_2001} present GeoPing. GeoPing will issue ping command to a given IP from a set of probes, generating a delay vector. This delay vector is then compared with the results from a set of landmarks of known locations. The location of the landmark whose result is most close to that of the given IP is used as the output. They use US universities as test-set.

\cite{gueye_2006} presents Constraint-based Geolocation (CBG), which use triangulation-like technique to locate a given IP. The authors assume there is a linear relationship between delay and distance, then estimate the coefficient between them. This coefficient is then used to calculate the distance between known landmarks and target. They use PlanetLab nodes as test-set.

\cite{katzbassett_2006,hu_2012} use Topology-based Geolocation (TBG) to geolocate a given IP address. The accuracy is claimed to be within 67km. TBG works by finding positions for the target and the routers such that the distance between the positions of every pair of adjacent network elements is proportional to the corresponding link latency measurement. The authors also claim an upperbound of $\frac{4}{9}c$ can be used to bound the relationship between intervals and distance, where $c$ is the speed of light. 

\cite{hu_2012}'s work is most close to what we want to do. Using the same method in \cite{katzbassett_2006}, but by restricting the size of routers referred, the authors applied TBG to millions of IPs and generate an geo-location map that covers around 35\% of IPv4 address space.

\section{Problems}

Geolocation data is known to have problems of inaccuracy.

\cite{calder_2013} use ping to estimate network latency, which is against the result mentioned in \cite{pelsser_2013}

\section{Collecting Data using traceroute}


\section{Related Works}

\section{Conclusion}

%--------------------------------------------------------------------
\bibliographystyle{plain}
\bibliography{reference}
\end{document}